video discover phase 2: blended 'Recommended for you' wall
A single personalized wall aggregating TMDB recommendations across many of your owned titles (random_owned_titles seeds), ranked by consensus — a title recommended by more of your library ranks higher (ties by rating then popularity), owned + seed titles excluded. - core/video/discovery_recs.py: pure blend_recommendations (dedup/consensus/exclude), 7 tests. - /api/video/discover/foryou aggregates ~12 seeds' recommendations. - loadForYou() prepends the 'Recommended for you' rail on top of the stack; re-runs on the hide-owned toggle.
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BoulderBadgeDad
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4 changed files with 173 additions and 0 deletions
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@ -85,6 +85,31 @@ def register_routes(bp):
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logger.exception("discover morelike failed")
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return jsonify({"rails": []})
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@bp.route("/discover/foryou", methods=["GET"])
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def video_discover_foryou():
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"""A single 'Recommended for you' wall blended from many owned titles — a title
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recommended by more of your library ranks higher (consensus)."""
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from . import get_video_db
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from core.video.enrichment.engine import get_video_enrichment_engine
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from core.video.discovery_recs import blend_recommendations
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try:
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from core.video.sources import resolve_video_server
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srv = resolve_video_server()
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except Exception:
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srv = None
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db = get_video_db()
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eng = get_video_enrichment_engine()
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try:
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seeds = db.random_owned_titles(6, srv) # up to 6 movies + 6 shows
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seed_ids = [s["tmdb_id"] for s in seeds if s.get("tmdb_id")]
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rec_lists = [eng.recommendations(s["kind"], s["tmdb_id"])
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for s in seeds if s.get("tmdb_id")]
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items = blend_recommendations(rec_lists, exclude_ids=seed_ids, limit=40)
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return jsonify({"items": items})
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except Exception:
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logger.exception("discover foryou failed")
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return jsonify({"items": []})
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@bp.route("/discover/gaps", methods=["GET"])
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def video_discover_gaps():
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"""'What am I missing?' rails — franchises you've started but not finished, and
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71
core/video/discovery_recs.py
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71
core/video/discovery_recs.py
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@ -0,0 +1,71 @@
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"""Blend per-title TMDB recommendations into one ranked "Recommended for you" wall.
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The discover page already does per-title rails ("More like Dune"). This aggregates the
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recommendations of MANY owned titles into a single personalized wall: a candidate
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recommended by *more* of your titles ranks higher (consensus is a stronger signal than
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any one seed), ties broken by rating then popularity. Owned titles (the engine annotates
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each recommendation with ``library_id`` when owned) and the seed titles themselves are
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excluded, so the wall is all stuff you don't have.
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Pure + I/O-free: the discover API fetches each seed's recommendations (cached) and passes
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the lists here, so the dedup/consensus ranking is unit-testable without TMDB.
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"""
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from __future__ import annotations
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from typing import Any, Dict, Iterable, List
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def blend_recommendations(
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rec_lists: List[List[Dict[str, Any]]],
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*,
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exclude_ids: Iterable = (),
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limit: int = 40,
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) -> List[Dict[str, Any]]:
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"""Aggregate ``rec_lists`` (one recommendation list per seed title) into a single
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ranked, deduped list of un-owned titles.
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Each item is a dict with ``tmdb_id`` / ``kind`` (and optionally ``library_id`` set by
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the engine when owned, plus ``rating`` / ``popularity``). A title appearing across more
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seed lists scores higher; ties fall back to rating then popularity. Owned items
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(``library_id`` not None) and ``exclude_ids`` (the seeds) are dropped. ``limit`` caps
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the result (0 = all).
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"""
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exclude = set()
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for x in exclude_ids or []:
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try:
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exclude.add(int(x))
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except (TypeError, ValueError):
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continue
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agg: Dict[tuple, Dict[str, Any]] = {}
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for lst in rec_lists or []:
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for it in lst or []:
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if not isinstance(it, dict):
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continue
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if it.get("library_id") is not None: # owned (engine-annotated) — skip
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continue
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tid = it.get("tmdb_id")
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try:
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tid = int(tid)
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except (TypeError, ValueError):
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continue
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if tid in exclude:
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continue
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key = (it.get("kind"), tid)
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entry = agg.get(key)
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if entry is None:
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agg[key] = {"item": it, "count": 1}
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else:
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entry["count"] += 1
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ranked = sorted(
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agg.values(),
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key=lambda e: (e["count"], e["item"].get("rating") or 0, e["item"].get("popularity") or 0),
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reverse=True,
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)
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items = [e["item"] for e in ranked]
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return items[:limit] if limit and limit > 0 else items
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__all__ = ["blend_recommendations"]
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51
tests/video/test_discovery_recs.py
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51
tests/video/test_discovery_recs.py
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@ -0,0 +1,51 @@
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"""Blended 'Recommended for you' aggregation (#discover phase 2)."""
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from __future__ import annotations
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from core.video.discovery_recs import blend_recommendations
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def _it(tid, kind="movie", rating=0, pop=0, owned=False):
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d = {"tmdb_id": tid, "kind": kind, "rating": rating, "popularity": pop}
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if owned:
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d["library_id"] = 99
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return d
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def test_consensus_ranks_higher():
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# title 2 recommended by 3 seeds, title 1 by 1 -> title 2 first
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lists = [[_it(1), _it(2)], [_it(2)], [_it(2), _it(3)]]
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out = blend_recommendations(lists)
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assert [i["tmdb_id"] for i in out][0] == 2
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def test_excludes_owned_and_seeds():
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lists = [[_it(1), _it(2, owned=True), _it(3)]]
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out = blend_recommendations(lists, exclude_ids=[1])
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assert [i["tmdb_id"] for i in out] == [3] # 1 = seed, 2 = owned
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def test_ties_break_by_rating_then_popularity():
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lists = [[_it(1, rating=7, pop=10), _it(2, rating=9, pop=5), _it(3, rating=9, pop=50)]]
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# all count=1 -> rating desc (3,2 tie at 9 -> pop desc: 3 then 2), then 1
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assert [i["tmdb_id"] for i in blend_recommendations(lists)] == [3, 2, 1]
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def test_dedup_same_title_across_lists_counts_once_per_list():
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lists = [[_it(5)], [_it(5)], [_it(5)]]
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out = blend_recommendations(lists)
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assert len(out) == 1 and out[0]["tmdb_id"] == 5
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def test_kind_distinguishes_same_tmdb_id():
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lists = [[_it(7, kind="movie"), _it(7, kind="show")]]
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assert len(blend_recommendations(lists)) == 2
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def test_limit():
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lists = [[_it(i, pop=i) for i in range(1, 11)]]
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assert len(blend_recommendations(lists, limit=3)) == 3
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def test_empty():
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assert blend_recommendations([]) == []
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assert blend_recommendations([[], None]) == []
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@ -309,6 +309,30 @@
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.catch(function () { /* gaps are best-effort */ });
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}
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// ── "Recommended for you" — one wall blended from across your library, prepended on top ─
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function loadForYou() {
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fetch('/api/video/discover/foryou', { headers: { Accept: 'application/json' } })
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.then(function (r) { return r.ok ? r.json() : null; })
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.then(function (d) {
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var items = (d && d.items) || [];
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var host = $('[data-vdsc-shelves]');
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if (items.length < 6 || !host) return;
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var html = '<section class="vdsc-shelf vdsc-shelf--in vdsc-shelf--foryou" data-vdsc-loaded="1">' +
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'<div class="vdsc-shelf-head">' +
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'<h3 class="vdsc-shelf-title">Recommended for you</h3>' +
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'<div class="vdsc-shelf-nav">' +
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'<button class="vdsc-arrow" type="button" data-vdsc-scroll="-1" aria-label="Scroll left">‹</button>' +
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'<button class="vdsc-arrow" type="button" data-vdsc-scroll="1" aria-label="Scroll right">›</button>' +
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'</div>' +
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'</div>' +
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'<div class="vdsc-rail" data-vdsc-rail>' + items.map(card).join('') + '</div>' +
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'</section>';
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host.insertAdjacentHTML('afterbegin', html);
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hydrateGet(host);
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})
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.catch(function () { /* best-effort */ });
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}
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// ── shelves (lazy rails) ──────────────────────────────────────────────────
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function renderShelves() {
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var host = $('[data-vdsc-shelves]'); if (!host) return;
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@ -527,6 +551,7 @@
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renderShelves();
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loadMoreLike();
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loadGaps();
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loadForYou();
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});
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}
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// Infinite scroll: a sentinel near the grid bottom pulls the next page.
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@ -565,6 +590,7 @@
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renderShelves();
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loadMoreLike(); // prepend personalized 'More like…' rails when ready
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loadGaps(); // prepend 'what am I missing' (franchise + person) gap rails
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loadForYou(); // prepend the blended 'Recommended for you' wall (sits on top)
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});
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}
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function showEmpty() {
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